The schematic diagram of the iForest algorithm.
<div><p>Anomaly detection is widely used in cold chain logistics (CCL). But, because of the high cost and technical problem, the anomaly detection performance is poor, and the anomaly can not be detected in time, which affects the quality of goods. To solve these problems, the paper pres...
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2025
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| _version_ | 1852022217102065664 |
|---|---|
| author | Zhibo Xie (6790775) |
| author2 | Heng Long (157361) Chengyi Ling (20854893) Yingjun Zhou (2350510) Yan Luo (255674) |
| author2_role | author author author author |
| author_facet | Zhibo Xie (6790775) Heng Long (157361) Chengyi Ling (20854893) Yingjun Zhou (2350510) Yan Luo (255674) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zhibo Xie (6790775) Heng Long (157361) Chengyi Ling (20854893) Yingjun Zhou (2350510) Yan Luo (255674) |
| dc.date.none.fl_str_mv | 2025-03-10T17:33:28Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0315322.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_schematic_diagram_of_the_iForest_algorithm_/28567764 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biochemistry Developmental Biology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified cold chain logistics relation coefficient ρjk isolated forest algorithm div >< p anomaly detection scheme anomaly detection performance average f1 score anomaly detection performance indicators new scheme improved algorithm f1 </ correlation coefficient three types technical problem slightly longer sliding window recall ), r </ precision ), paper presents p </ mathematical model lof ), iforest ). high cost data stream data increases data flow cross factor collected data abnormal events |
| dc.title.none.fl_str_mv | The schematic diagram of the iForest algorithm. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Anomaly detection is widely used in cold chain logistics (CCL). But, because of the high cost and technical problem, the anomaly detection performance is poor, and the anomaly can not be detected in time, which affects the quality of goods. To solve these problems, the paper presents a new anomaly detection scheme for CCL. At first, the characteristics of the collected data of CCL are analyzed, the mathematical model of data flow is established, and the sliding window and correlation coefficient are defined. Then the abnormal events in CCL are summarized, and three types of abnormal judgment conditions based on cor-relation coefficient ρjk are deduced. A measurement anomaly detection algorithm based on the improved isolated forest algorithm is proposed. Subsampling and cross factor are designed and used to overcome the shortcomings of the isolated forest algorithm (iForest). Experiments have shown that as the dimensionality of the data increases, the performance indicators of the new scheme, such as <i>P</i> (precision), <i>R</i> (recall), <i>F1</i> score, and AUC (area under the curve), become increasingly superior to commonly used support vector machines (SVM), local outlier factors (LOF), and iForests. Its average <i>P</i> is 0.8784, average <i>R</i> is 0.8731, average F1 score is 0.8639, and average AUC is 0.9064. However, the execution time of the improved algorithm is slightly longer than that of the iForest.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_8a73901dcd23a315d404fcec11d877bb |
| identifier_str_mv | 10.1371/journal.pone.0315322.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28567764 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The schematic diagram of the iForest algorithm.Zhibo Xie (6790775)Heng Long (157361)Chengyi Ling (20854893)Yingjun Zhou (2350510)Yan Luo (255674)BiochemistryDevelopmental BiologyScience PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedcold chain logisticsrelation coefficient ρjkisolated forest algorithmdiv >< panomaly detection schemeanomaly detection performanceaverage f1 scoreanomaly detectionperformance indicatorsnew schemeimproved algorithmf1 </correlation coefficientthree typestechnical problemslightly longersliding windowrecall ),r </precision ),paper presentsp </mathematical modellof ),iforest ).high costdata streamdata increasesdata flowcross factorcollected dataabnormal events<div><p>Anomaly detection is widely used in cold chain logistics (CCL). But, because of the high cost and technical problem, the anomaly detection performance is poor, and the anomaly can not be detected in time, which affects the quality of goods. To solve these problems, the paper presents a new anomaly detection scheme for CCL. At first, the characteristics of the collected data of CCL are analyzed, the mathematical model of data flow is established, and the sliding window and correlation coefficient are defined. Then the abnormal events in CCL are summarized, and three types of abnormal judgment conditions based on cor-relation coefficient ρjk are deduced. A measurement anomaly detection algorithm based on the improved isolated forest algorithm is proposed. Subsampling and cross factor are designed and used to overcome the shortcomings of the isolated forest algorithm (iForest). Experiments have shown that as the dimensionality of the data increases, the performance indicators of the new scheme, such as <i>P</i> (precision), <i>R</i> (recall), <i>F1</i> score, and AUC (area under the curve), become increasingly superior to commonly used support vector machines (SVM), local outlier factors (LOF), and iForests. Its average <i>P</i> is 0.8784, average <i>R</i> is 0.8731, average F1 score is 0.8639, and average AUC is 0.9064. However, the execution time of the improved algorithm is slightly longer than that of the iForest.</p></div>2025-03-10T17:33:28ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0315322.g003https://figshare.com/articles/figure/The_schematic_diagram_of_the_iForest_algorithm_/28567764CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285677642025-03-10T17:33:28Z |
| spellingShingle | The schematic diagram of the iForest algorithm. Zhibo Xie (6790775) Biochemistry Developmental Biology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified cold chain logistics relation coefficient ρjk isolated forest algorithm div >< p anomaly detection scheme anomaly detection performance average f1 score anomaly detection performance indicators new scheme improved algorithm f1 </ correlation coefficient three types technical problem slightly longer sliding window recall ), r </ precision ), paper presents p </ mathematical model lof ), iforest ). high cost data stream data increases data flow cross factor collected data abnormal events |
| status_str | publishedVersion |
| title | The schematic diagram of the iForest algorithm. |
| title_full | The schematic diagram of the iForest algorithm. |
| title_fullStr | The schematic diagram of the iForest algorithm. |
| title_full_unstemmed | The schematic diagram of the iForest algorithm. |
| title_short | The schematic diagram of the iForest algorithm. |
| title_sort | The schematic diagram of the iForest algorithm. |
| topic | Biochemistry Developmental Biology Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified cold chain logistics relation coefficient ρjk isolated forest algorithm div >< p anomaly detection scheme anomaly detection performance average f1 score anomaly detection performance indicators new scheme improved algorithm f1 </ correlation coefficient three types technical problem slightly longer sliding window recall ), r </ precision ), paper presents p </ mathematical model lof ), iforest ). high cost data stream data increases data flow cross factor collected data abnormal events |